This paper proposes a deep neural network (DNN) based method to solve the multicell power control problem that maximizes the sum rate subject to per-user rate constraints. The basic idea is to employ a two-DNN concatenating network structure, where the second DNN associated with a randomization processing is designed to guarantee the per-user rate constraints via supervised learning, given which the first DNN is trained to directly maximize the sum rate by unsupervised learning. Simulation results demonstrate that the proposed method can achieve better performance with low complexity compared to existing deep learning and numerical optimization methods.


    Zugriff

    Zugriff prüfen

    Verfügbarkeit in meiner Bibliothek prüfen

    Bestellung bei Subito €


    Exportieren, teilen und zitieren



    Titel :

    Constrained learning for Multicell Power Control


    Beteiligte:
    Li, Yinghan (Autor:in) / Han, Shengqian (Autor:in) / Yang, Chenyang (Autor:in)


    Erscheinungsdatum :

    01.04.2021


    Format / Umfang :

    2273762 byte





    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch



    Multicell Multicast with Joint Beamforming and Power Allocation

    Hsu, Guan-Wen / Liao, Shuyu / Su, Hsuan-Jung et al. | IEEE | 2015



    Active Balancing Circuit Of Multicell Battery

    SHIN JONG WOO / YUN YEONG RYONG | Europäisches Patentamt | 2018

    Freier Zugriff

    Shear flows in multicell sandwich sections

    Benscoter, Stanley U | NTRS | 1948


    Shear flows in multicell sandwich sections

    Benscoter, Stanley U. | TIBKAT | 1948